Life cycle cost analysis to select types of new pavement considering the climate change
Bibliographic record
Abstract
A series of life cycle cost analysis (LCCA) was performed among three candidate pavement structures in Canada: asphalt, concrete, and composite pavements. Asphalt pavement construction demonstrated the lowest user cost owing to its shorter construction duration and day-only work schedule. Conversely, concrete pavement boasts the lowest agency cost attributed to its superior construction durability. Considering that Route 261 primarily serves newly established industries, which often adhere to stringent production schedules, it is advisable to opt for the construction of asphalt pavement, given its lower user cost. Over time, this decision is also expected to yield incremental monetary value. Nevertheless, if the influence of climate change on various pavement types is considered into the LCCA, the outcomes may diverge. It could be imperative to account for weather fluctuations, as they can significantly hasten pavement surface degradation. The region of Quebec, Canada has seen an increase in the intensity of precipitation events and more fluctuations in temperature, possibly contributing to cracking roadways. Past studies indicated that climate change, particularly temperature and precipitation contribute to accelerating cracking in flexible pavements. Climate variables such as temperature, precipitation wind, cloud cover and groundwater levels and freeze-thaw cycles exert distinct impacts on pavement performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".